LLLR Parsing: a Combination of LL and LR Parsing

A new parsing method called LLLR parsing is defined and a method for producing LLLR parsers is described. An LLLR parser uses an LL parser as its backbone and parses as much of its input string using LL parsing as possible. To resolve LL conflicts it triggers small embedded LR parsers. An embedded LR parser starts parsing the remaining input and once the LL conflict is resolved, the LR parser produces the left parse of the substring it has just parsed and passes the control back to the backbone LL parser. The LLLR(k) parser can be constructed for any LR(k) grammar. It produces the left parse of the input string without any backtracking and, if used for a syntax-directed translation, it evaluates semantic actions using the top-down strategy just like the canonical LL(k) parser. An LLLR(k) parser is appropriate for grammars where the LL(k) conflicting nonterminals either appear relatively close to the bottom of the derivation trees or produce short substrings. In such cases an LLLR parser can perform a significantly better error recovery than an LR parser since the most part of the input string is parsed with the backbone LL parser. LLLR parsing is similar to LL(^*) parsing except that it (a) uses LR(k) parsers instead of finite automata to resolve the LL(k) conflicts and (b) does not perform any backtracking

Geometry meets semantics for semi-supervised monocular depth estimation

Depth estimation from a single image represents a very exciting challenge in computer vision. While other image-based depth sensing techniques leverage on the geometry between different viewpoints (e.g., stereo or structure from motion), the lack of these cues within a single image renders ill-posed the monocular depth estimation task. For inference, state-of-the-art encoder-decoder architectures for monocular depth estimation rely on effective feature representations learned at training time. For unsupervised training of these models, geometry has been effectively exploited by suitable images warping losses computed from views acquired by a stereo rig or a moving camera. In this paper, we make a further step forward showing that learning semantic information from images enables to improve effectively monocular depth estimation as well. In particular, by leveraging on semantically labeled images together with unsupervised signals gained by geometry through an image warping loss, we propose a deep learning approach aimed at joint semantic segmentation and depth estimation. Our overall learning framework is semi-supervised, as we deploy groundtruth data only in the semantic domain. At training time, our network learns a common feature representation for both tasks and a novel cross-task loss function is proposed. The experimental findings show how, jointly tackling depth prediction and semantic segmentation, allows to improve depth estimation accuracy. In particular, on the KITTI dataset our network outperforms state-of-the-art methods for monocular depth estimation.Comment: 16 pages, Accepted to ACCV 201

Jäsentämisen strategiat

Ohjelmistokääntäjät lukeutuvat kaiken nykyaikaisen ohjelmoinnin kivijalkoihin. Osa ohjelmointikielistä toimii siten, että ne käännetään annetulta koodikieleltä konekieleksi. Toisaalta jotkut ohjelmointikielet toimivat siten, että lähdekoodia lukee reaaliaikaisesti ohjelmistotulkki, joka itse on konekieleksi käännetty ohjelmisto. Lisäksi on vielä ohjelmointikieliä, joita käännetään jonkinlaiseksi välikieleksi, jota sitten tulkataan konekieleksi. Hyvin harva kuitenkaan on täysin perillä siitä, miten kääntäjät toimivat. Tässä työssä tutkitaan yleisimpiä menetelmiä eräästä tietystä kääntämisen vaiheesta: jäsentämisestä. On haluttu tietää, miten kääntäjät pilkkovat koodia, miten käsittelevät sitä ja millaiseen muotoon se jäsennellään. Työssä kuvaillaan ensin yksityiskohtaisesti jäsentimien toiminnan periaatteita, teoriaa ja historiaa, minkä jälkeen perehdytään kaikkein eniten käytetyn jäsentimen toimintaan. Aihetta tutkiessa saatiin selville, että jäsentimet jakaantuvat karkeasti kahteen kategoriaan: ylhäältä-alas ja alhaalta-ylös -tyyppeihin. Kumpikin näistä jakautuu edelleen alatyyppeihin, jotka ovat toinen toistaan tehokkaampia. Ne ovat myös entistä monimutkaisempia ja soveltuvat yhä laajemman ohjelmointikielten kirjon kääntämiseen. Havaittiin, että jäsentimien suunnittelussa on vankka tiede, joka on muodostunut purkamaan korkeamman tason ohjelmoinnin abstraktioita ja vastaamaan asiaan liittyviin teknisiin haasteisiin. Kaikkein kehittyneimmissä jäsentimissä käytetään matemaattisloogisia merkintätapoja kuvailemaan jäsentimien toimintaa

Framework development for providing accessibility to qualitative spatial calculi

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Qualitative spatial reasoning deals with knowledge about an infinite spatial domain using a finite set of qualitative relations without using numerical computation. Qualitative knowledge is relative knowledge where we obtain the knowledge on the basis of comparison of features with in the object domain rather then using some external scales. Reasoning is an intellectual facility by which, conclusions are drawn from premises and is present in our everyday interaction with the geographical world. The kind of reasoning that human being relies on is based on commonsense knowledge in everyday situations. During the last decades a multitude of formal calculi over spatial relations have been proposed by focusing on different aspects of space like topology, orientation and distance. Qualitative spatial reasoning engines like SparQ and GQR represents space and reasoning about the space based on qualitative spatial relations and bring qualitative reasoning closer to the geographic applications. Their relations and certain operations defined in qualitative calculi use to infer new knowledge on different aspects of space. Today GIS does not support common-sense reasoning due to limitation for how to formalize spatial inferences. It is important to focus on common sense geographic reasoning, reasoning as it is performed by human. Human perceive and represents geographic information qualitatively, the integration of reasoner with spatial application enables GIS users to represent and extract geographic information qualitatively using human understandable query language. In this thesis, I designed and developed common API framework using platform independent software like XML and JAVA that used to integrate qualitative spatial reasoning engines (SparQ) with GIS application. SparQ is set of modules that structured to provides different reasoning services. SparQ supports command line instructions and it has a specific syntax as set of commands. The developed API provides interface between GIS application and reasoning engine. It establishes connection with reasoner over TCP/IP, takes XML format queries as input from GIS application and converts into SparQ module specific syntax. Similarly it extracts given result, converts it into defined XML format and passes it to GIS application over the same TCP/IP connection. The most challenging part of thesis was SparQ syntax analysis for inputs and their outputs. Each module in Sparq takes module specific query syntax and generates results in multiple syntaxes like; error, simple result and result with comments. Reasoner supports both binary and ternary calculi. The input query syntax for binary-calculi is different for ternary-calculi in the terms of constraint-networks. Based on analysis I, identified commonalities between input query syntaxes for both binary and ternary calculi and designed XML structures for them. Similarly I generalized SparQ results into five major categories and designed XML structures. For ternary-calculi, I considered constraint-reasoning module and their specific operations and designed XML structure for both of their inputs and outputs

Systems analysis of the space shuttle

Developments in communications systems, computer systems, and power distribution systems for the space shuttle are described. The use of high speed delta modulation for bit rate compression in the transmission of television signals is discussed. Simultaneous Multiprocessor Organization, an approach to computer organization, is presented. Methods of computer simulation and automatic malfunction detection for the shuttle power distribution system are also described

Evaluation of an Esperanto-Based Interlingua Multilingual Survey Form Machine Translation Mechanism Incorporating a Sublanguage Translation Methodolgy

Translation costs restrict the preparation of medical survey and other questionnaires for migrant communities in Western Australia. This restriction is compounded by a lack of affordable and accurate machine translation mechanisms. This research investigated and evaluated combined strategies intended to provide an efficacious and affordable machine translator by: • using an interlingua or pivot-language that requires less resources for its construction than contemporary systems and has the additional benefit of significant error reduction; and • defining smaller lexical environments to restrict data, thereby reducing the complexity of translation rules and enhancing correct semantic transfer between natural languages. This research focussed on producing a prototype machine translation mechanism that would accept questionnaire texts as discrete questions and suggested answers from which a respondent may select. The prototype was designed to accept non-ambiguous English as the source language, translate it to a pivot-language or interlingua, Esperanto, and thence to a selected target language, French. Subsequently, a reverse path of translation from the target language back to the source language enabled validation of minimal or zero change in both syntax and semantics of the original input. Jade, an object-oriented (00) database application, hosting the relationship between the natural languages and the interlingua, was used to facilitate the accurate transfer of meaning between the natural languages. Translation, interpretation and validation of sample texts was undertaken by linguists qualified in English, French and Esperanto. Translation output from the prototype model was compared, again with assistance from linguists, with a \u27control\u27 model, the SYSTRAN On-Line Translator, a more traditional transfer translation product. Successful completion of this research constitutes a step towards an increased availability of low cost machine translation to assist in the development of reliable and efficient survey translation systems for use in specific user environments. These environments include, but arc not exclusive to, medical, hospital and Australian indigenous-contact environments

A Translational Investigation of Reinforced Behavioral Variability: Implications for Promoting Behavioral Variability in Individuals with Autism Spectrum Disorder

Behavioral variability is sometimes adaptive and can be maintained by the delivery of reinforcement. Individuals with autism spectrum disorder (ASD) often show restricted and repetitive behaviors. Therefore, interventions to promote behavioral variability in individuals with ASD are needed. The present line of research was designed to inform such interventions by investigating reinforced behavioral variability from basic, applied, and translational perspectives. Each of these laboratory studies involved participants making sequences of well-defined responses, which were compared to previous responses. Responses that meet a variability contingency (i.e., were sufficiently different from previous responses) produced rewards. Studies 1 and 2 were basic experiments, in which we demonstrated a recurrence of reinforced behavioral variability in pigeons and college students, respectively. Study 3 was an applied experiment designed to assess choice for variability in children with ASD. Study 4 was a translational experiment investigating the viability of a rat model of ASD. This translational line of research should be continued to better understand reinforced behavioral variability and its implications for ASD